What are the most important turning points in a data scientist’s journey? — It is not easy to tell when does exactly a data science cadet becomes an actual data scientist. Unlike in the army, we do not have distinct ranks that would correlate with seniority. Neither is a career path of a data scientist linear. …

On the benefits of building trust, precision, and awareness of one’s skills — I want to expand this work into a series of µ-tutorial videos. If you’re interested, please subscribe to my newsletter to stay in touch. The tech world surrounds us. It penetrates nearly every aspect of our lives not only through devices and services that we use, but it also affects…

I want to expand this work into a series of µ-tutorial videos. If you’re interested, please subscribe to my newsletter to stay in touch. Introduction Data analysis is one of the most essential steps in any data-related project. Regardless of the context (e.g. business, machine-learning, physics, etc.), there are many ways…

Simple usage of a reverse SSH tunnel for to greatly improve the usability. — Introduction It’s been quite some time since we wrote on any “engineering-like” topic. As we all want to stay efficient and productive, it is a good time to revisit Google Colaboratory. Google Colaboratory, or Colab for short, has been a great platform for data scientists or machine-learning enthusiasts in general. It…

Use factory pattern to design the code and avoid the data leakage — Introduction Autoregressive models are among the “more difficult” to implement when it comes to data preparation. As the models are expected to yield daily or hourly predictions, the datasets themselves are expected to grow and expand with every new observation requiring the models to undergo regular retraining. …

A small-scale convenient approach to scheduling ETL tasks with python. — Introduction Python has become an all-purpose language. It is especially commonly used in analytics and solving algorithmic problems within data science but is also popular in web development. This combination makes it a reasonable choice for various extract-transform-load (ETL) tasks. However, many of these tasks are rather small and don’t require…

Python Pandas and SQL — time aggregations and syntax explained. — Introduction Working with time-series data is often a challenge on its own. It is a special kind of data, where data points depend on each other across time. When analyzing it, your productivity at gaining insights to a large extent depends on your ability to juggle with the time dimension. Very…

A few tricks on how to learn quickly — especially in the Data Science world. — Technology is fascinating. It is the missing link that brings tomorrow’s dreams today. It is also an attractive career for many — engineers, managers, entrepreneurs, and all kinds of problem solvers are welcome. Unfortunately, there is also a catch. The catch is the ever-changing technology landscape in combination with an…

Data analysis is by far one of the most frequently occurring tasks in both business and technology. It is the daily bread of thousands of academic researchers, bankers, engineers, and, obviously, data scientists. Interestingly, despite being such a popular activity, there seems to be no agreement on what should be…